Preventing and Combatting Domestic Violence: Incrementalism and Interest Groups in Ukrainian Public Policy
Bibliographic record
Abstract
Statistical data indicates that domestic violence in present-day Ukraine is a particularly acute phenomenon. Urgent policy responses are therefore required on the part of state authorities in order to prevent and combat the problem. Moreover, Ukraine must improve its legislation in this regard in order to meet international obligations and achieve legislative approximation with the European Union (EU) in connection with EU membership. But for eleven years until June 2022, Ukraine underwent significant struggles in this sphere and was unable to ratify the Council of Europe Convention on Preventing and Combating Violence against Women and Domestic Violence (the Istanbul Convention). This article turns to incrementalism and interest-group analysis for an exploration of both the challenges in ratifying the Istanbul Convention in Ukraine and the policy-making process that was adopted on account of those challenges. Using the case of domestic-violence legislation in Ukraine and the issue of the ratification of the Istanbul Convention, the authors contend that incrementalism remains a viable policy-making practice, as it considers a variety of stakeholders (including interest groups) and promotes progress by fostering common-ground approaches and gradual improvements. Ukraine’s trajectory ultimately shifted when the Istanbul Convention was ratified in 2022. Diverging from incrementalism in such a way, though, risks reversing crucial changes because the local opposition of interest groups in relation to a major decision remains unresolved. This article, first, reviews Ukraine’s policy path and show that it was incremental prior to 2022. Then, it looks at interest groups and examines their arguments for and against ratification of the Istanbul Convention. Afterward, the authors address the Europeanization of Ukraine and its impact on the adoption of legislation related to domestic violence. Finally, the article discusses how ratification became possible in 2022 and how EU conditionality both contributed to realizing that goal and created potential risks for the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".